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In this article we describe our five-year research project on the notorious radical free speech service and fringe platform Gab. During these years we scraped an entire platform, prepared it into a dataset for analysis, and opened it up to a broader community of students and researchers. Each of these projects provides us not just with a small slice of platformized far-right culture but also with a larger sphere of a fringe platform. However, the overarching goal of the Gab project was to contribute to a methodology for the study of the contemporary platformized far right. The atypical nature of the project posed many methodological, epistemological, and legal challenges. It therefore kicked off an institutional learning process about the possibilities, legal boundaries, and best practices for research compliant with General Data Protection Regulation (GDPR). In this article we argue that the study of the platformized far right should have a thorough understanding of the medium on which the object is present, as well as the methods with which the object is captured. What is more, scholars that use digital tools and data methods for capture and analysis of web platforms must become literate in operating them. Consequently, data-driven research on the far right is naturally interdisciplinary and therefore cooperative and adherent to the principles of open science.

Drawing upon literature and theorising in relation to the sociology of expectations and critical data studies, we elaborate perceptions of the nature of dashboard data in one state educational jurisdiction in Australia. Our research utilises interviews with senior educational bureaucrats, who engaged with a new dashboard, and the data it generated, during the initial stages of the development and implementation of the dashboard. This included personnel involved in the technical development of the dashboard, through to those who were responsible for engaging with such data to help facilitate enhanced school organisational, teaching and learning practices across the state. The research reveals that at the same time as data provided through the dashboard were understood by educators as more current and potentially beneficial for making sense of students' learning, such data also simultaneously conveyed information that was potentially not as accurate, accessible, or timely as desired. Consequently, such dashboards are complex sites of ‘great expectations', in which positive futures about dashboard data are expressed, even as reservations are simultaneously articulated. While such dashboards enhance access of information to educators in a more ‘timely’ fashion, there is a need for further scrutiny into such platforms and the veracity of the data generated. [ABSTRACT FROM AUTHOR]

The article critically examines the implementation and impact of the Digital Urban Waste Tracking System (DUWTS), a 'smart' waste management technology deployed in several towns in Poland to enhance urban waste segregation practices. By integrating critical discard studies with critical data studies, the article introduces the concept of 'waste data reflectivity' to investigate how data representation in DUWTS influences perceptions and responsibilities within waste management. The study highlights how DUWTS employs advanced dataveillance technologies to monitor waste disposal, creating distorted reflections that obscure the complexities of municipal waste management. These distortions are analyzed through the metaphorical lenses of three mirrors – concave, kaleidoscopic, and ceiling – which reveal the illusory effects on municipalities, waste disposal companies, and residents. Additionally, the article discusses how DUWTS exemplifies 'technologies of (un)knowing', which systematically obscure or misrepresent waste management issues, leading to the marginalization of true responsibilities and challenges. The findings demonstrate that while DUWTS represents a technological advancement, it can also perpetuate the existing challenges in waste management by masking the true nature of waste issues and obscuring the responsibilities of different stakeholders. The article concludes by emphasizing the socio-technological consequences of such systems and the importance of a critical analysis in evaluating the effectiveness and ethics of data-driven waste management technologies. [ABSTRACT FROM AUTHOR]

Prior scholarship argues that facts derived from data are not separate from their contexts and values. In this study of a data journalism team, I define and apply a sociotechnical network approach to stasis that maps their rhetorical actions with their quantitative work. The stasis network methodology identified how their process confronted competing definitions of metrics, which impacted their sense of what was significant and ethically possible, when developing the goals for their report. [ABSTRACT FROM AUTHOR]

This article investigates local government cultural economy data practices in England (UK). Engaging with academic literature and policy reports, it highlights the following issues relating to these data practices: types of data; the context and drivers for using data; the possibilities and challenges of data volume; and training and expertise required to position data in relation to strategic decision-making. Engaging with the authors' research project with local government councillors and data officers working in local authorities in England, this article provides insights into situated experiences and working contexts. The analysis reveals specific challenges and opportunities around resources and capacity, decision-making and partnerships. These data practices and challenges are critically examined through perspectives from critical data studies and the data feminism principles of context and visibility. Insights and issues are raised around decision-making, intuition and capacity, expertise and gatekeeping and more encompassing concerns around data characteristics and social power. In response, approaches from data literacy, namely data biographies, are explored to inform interventions into local government cultural economy data practices. [ABSTRACT FROM AUTHOR]

Copyright of Sosiologia is the property of Westermarck Society and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The continuing digitization and datafication that our society is undergoing are having a significant impact on our daily lives, giving rise to new possibilities but also entailing significant risks for people who are discriminated against or marginalized. Queer communities are particularly affected by these processes; therefore, it is crucially relevant to research transnational digital projects that involve them. In the Wikidata Gender Diversity (WiGeDi) project, we are looking at practices of gender representation in the Wikidata knowledge base, a collaborative online project managed by a worldwide community. Working from the idea that gender is a complex social construct, we investigate how the Wikidata community has approached the complex issue of modeling and populating gender data, progressing from a very narrow interpretation of gender as a binary to a representation that is more inclusive of a multiplicity of gender identities. [ABSTRACT FROM AUTHOR]

Considering Artificial Intelligence systems as boundary objects, which are interdisciplinary objects sustained differently by diverse fields while providing shared discourses between them, this essay summarizes the approaches of examining bias in AI systems. It argues that examining each part related to the building and working of AI systems is essential for unpacking the political play and potential insert points of biases in them. It concentrates on the critical analysis of data and algorithms as two core parts of AI systems by operationalizing hermeneutic reverse engineering. Hermeneutic reverse engineering is a framework to unpack and understand different elements of a technocultural object and/or system that contribute to the construction of its meaning and contexts. It employs a speculative imagination of what other realities can be designed and includes cultural analysis to identify existing meanings and assumptions behind the technocultural object, identifying key elements of signification, and speculating possibilities of reassembling different meanings for the object. The main results obtained by this method on AI systems is using cultural consideration and technological imagination to unpack existing meanings created by AI and design innovative approaches for AI to exert alternate/ inclusive meanings. The research perspectives presented in this article include critical examination of biases and politics within different elements of AI systems, and the impact of these biases on different social groups. The paper proposes using the method of hermeneutic reverse engineering to investigate AI systems and speculate possible alternate and more accountable futures for AI systems.

Artificial intelligence is transforming the way we work with information across disciplines and practical contexts. A growing range of disciplines are now involved in studying, developing and assessing the use of AI in practice, but these disciplines often employ conflicting understandings of what AI is and what is involved in its use. New, interdisciplinary approaches are needed to bridge competing conceptualizations of AI in practice and help shape the future of AI use. I propose a novel conceptual framework called AI Thinking, which models key decisions and considerations involved in AI use across disciplinary perspectives. AI Thinking addresses five practice-based competencies involved in applying AI in context: motivating AI use, formulating AI methods, assessing available tools and technologies, selecting appropriate data and situating AI in the sociotechnical contexts it is used in. A hypothetical case study is provided to illustrate the application of AI Thinking in practice. This article situates AI Thinking in broader cross-disciplinary discourses of AI, including its connections to ongoing discussions around AI literacy and AI-driven innovation. AI Thinking can help to bridge between the work of diverse disciplines, contexts and actors in the AI space, and shape AI efforts in education, industrial development and policy.

The fullness of Black students' experiences in college has yet to be archived. The same can be said of Black people broadly, whose existence has long been reduced by and to what is observable, by systems of power and those at the helm. This is perhaps due to the structural and structural limitations of data collection efforts, or not of interest to decision-makers. Or perhaps, this is a symbol of the impossibility of capturing Black life beyond the physical. Regardless, Black life on college campuses across the globe is systematically reduced to standard institutional measures. This reduction is facilitated by the ongoing datafication of student success—the ways that students' success has been made metric and the ways student success as a socio-political phenomenon has been increasingly governed by data-related practices and discourses. As a result, Black students' material and data realities are (re)constructed, especially through student success data practices. [ABSTRACT FROM AUTHOR]

Despite being the target of much criticism, commercialised digital technologies have proliferated in reproductive health arenas. Fertility applications (apps) are now some of the most popular and ubiquitous digital health tracking technologies, with millions of downloads. Previous scholarship has already underlined the problematic nature of their design and surveillance features. However, less attention has been paid to the wider effects of datafied knowledge availability. This research specifically asks: How does the proliferation of fertility apps shape knowledge (and associated practices) in reproductive health? Drawing on an analysis of key document sources, I here argue that fertility apps act as mediators between stakeholders, data and datafied outputs, thus facilitating: (1) the datafication of fertility awareness knowledge and the production of new datafied knowledge, (2) legitimation discourses and practices and (3) the remaking of private/public expertise and knowledge production networks in reproductive health. To effectively analyse the effects of commercialised reproductive health apps, this work argues for an understanding of data technologies that is informed by critical data studies. [ABSTRACT FROM AUTHOR]

The ideal of unfettered data circulation has fallen into crisis. As of today, a growing number of actors are introducing measures to ensure a greater degree of control over the global data pipeline. Combining critical data studies and political theory, this article conceptualises the current technopolitical conjuncture as one of 'planetary data turbulence' in which divergences regarding the production and circulation of data have become the norm. The concept of data turbulence emerges from studies on data friction, but this article contends that the current state of affairs requires expanding the emphasis on technosciences and materiality in these works. Drawing on Ernesto Laclau and Chantal Mouffe, the article shows that attending to antagonism and discourse makes it possible to account for the eminently political forces shaping the circulation of data. The strengths of this framework are illustrated by looking at the articulation of digital sovereignty in different geographies. [ABSTRACT FROM AUTHOR]

With the emergence of social distancing, the shutdown of schools and restaurants, and increased anxieties about in- person farmers markets in early 2020, local farmers and food distributors quickly pivoted to digital tools to manage their farms and connect with buyers. In this paper, we explore the role of a seemingly simple digital tool in shaping alternative agrarian relations during the pandemic- induced local food boom: the spreadsheet. Through interviews with farmers and food distributors across urban, periurban, and rural landscapes in the United States (U.S.), we show how spreadsheets and other digital tools have helped farmers manage demand during the COVID- 19 pandemic while also presupposing costs, benefits, and efficiencies for these alternative agricultural food spaces. Ultimately, many local farmers found themselves pursuing goals of simplicity, labor efficiency, and expansion- oriented growth as they made sense of their farm data. Reflecting on these values, and the spreadsheet data underlying them, became a point of tension for farmers who place a high importance on diversification, stability, and interpersonal interactions. By attending to how these data are tracked, we gain deeper insights into how farmers and other stakeholders think about agrarian futures: who does the work, what gets planted, who's buying, and who benefits. [ABSTRACT FROM AUTHOR]

Surveillance of human subjects is how data-intensive companies obtain much of their data, yet surveillance increasingly meets with social and regulatory resistance. Data-intensive companies are thus seeking other ways to meet their data needs. This article explores one of these: the creation of synthetic data, or data produced artificially as an alternative to real-world data. I show that capital is already heavily invested in synthetic data. I argue that its appeal goes beyond circumventing surveillance to accord with a structural tendency within capitalism toward the autonomization of the circuit of capital. By severing data from human subjectivity, synthetic data contributes to the automation of the production of automation technologies like machine learning. A shift from surveillance to synthesis, I argue, has epistemological, ontological, and political economic consequences for a society increasingly structured around data-intensive capital. [ABSTRACT FROM AUTHOR]

Calling attention to the growing intersection between the insurance and technology sectors—or 'insurtech'—this article is intended as a bat signal for the interdisciplinary fields that have spent recent decades studying the explosion of digitization, datafication, smartification, automation, and so on. Many of the dynamics that attract people to researching technology are exemplified, often in exaggerated ways, by emerging applications in insurance, an industry that has broad material effects. Based on in-depth mixed-methods research into insurance technology, I have identified a set of interlocking logics that underly this regime of actuarial governance in society: ubiquitous intermediation, continuous interaction, total integration, hyper-personalization, actuarial discrimination, and dynamic reaction. Together these logics describe how enduring ambitions and existing capabilities are motivating the future of how insurers engage with customers, data, time, and value. This article surveys each logic, laying out a techno-political framework for how to orient critical analysis of developments in insurtech and where to direct future research on this growing industry. Ultimately, my goal is to advance our understanding how insurance—a powerful institution that is fundamental to the operations of modern society—continues to change, and what dynamics and imperatives, whose desires and interests are steering that change. The stuff of insurance is far too important to be left to the insurance industry. [ABSTRACT FROM AUTHOR]

In this special issue, we ask: What do we see when we look at datafied societies through the lens of care? Following the footsteps of feminist writers, activists, and academics who take care as a vantage point for scrutinising and reimaging technoscientific societies, this special issue brings together scholars from critical data studies who explore what we might learn (and see) when we apply care ethics to the study of datafication. To develop a view on datafied societies informed by ethics, concepts, and practices of care, we propose a move from critique to care in social studies of data-driven technologies. We specifically identify five moves in which a care lens provides a new perspective when studying datafication and datafied societies: (1) a move from data-driven technologies to socio-digital care arrangements, (2) a move from data science to data work and care, (3) a move from technical to situated modes of knowledge production, (4) a move from studying harms of datafication to the politics of vulnerability, and (5) a move towards building communities of care. Discussing how critical data studies and care ethics can mutually contribute to each other, this collection explores how this way of thinking can inform new ways of seeing datafied societies and imagine living and being well in more than human worlds nurtured by care. [ABSTRACT FROM AUTHOR]

This article unpacks the short-lived but momentous buzz around big data. Although talk about big data was once widespread, little is known about the efforts animating its semantics. Tracing this sociotechnical imaginary, we revisit how business insiders and IT commentators fueled the ephemeral yet potent excitement around the term. Our genealogical examination rests on a selection of publications from 2013 to 2017. We employ methods from critical discourse analysis to interrogate how big data was written into being and hyped into a topic of concern. In this aspirational discourse, tech evangelists and writers extrapolated from contexts in which large troves of data were already being harnessed to suggest that inescapable transformations were imminent. They sought to concretize abstract and unfathomable quantities while simultaneously overwhelming their readers with a sense of vastness that exceeds all contexts and outruns the most exuberant expectations. The term may have lost this luster, but big data technologies and practices are an integral part of today's technological infrastructures. [ABSTRACT FROM AUTHOR]

In this article, we reconsider elements of Agre's critical technical practice approach (Agre, 1997) for critical technical practice approach for reflexive artificial intelligence (AI) research and explore ways and expansions to make it productive for an operationalization in contemporary data science. Drawing on Jörg Niewöhner's co-laboration approach, we show how frictions within interdisciplinary work can be made productive for reflection. We then show how software development environments can be repurposed to infrastructure reflexivities and to make co-laborative engagement with AI-related technology possible and productive. We document our own co-laborative engagement with machine learning and highlight three exemplary critical technical practices that emerged out of the co-laboration: negotiating comparabilities, shifting contextual attention and challenging similarity and difference. We finally wrap up the conceptual and empirical elements and propose Reflexive Data Science (RDS) as a methodology for co-laborative engagement and infrastructured reflexivities in contemporary AI-related research. We come back to Agre's ways of operationalizing reflexivity and introduce the building blocks of RDS: (1) organizing encounters of social contestation, (2) infrastructuring a network of anchoring devices enabling reflection, (3) negotiating timely matters of concern and (4) designing for reflection. With our research, we aim at contributing to the methodological underpinnings of epistemological and social reflection in contemporary AI research. [ABSTRACT FROM AUTHOR]

Copyright of ESSACHESS is the property of ESSACHESS and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This project interrogates the United States' national fixation on the answer to the question: Who are you? In this article, it is posed that identity documentation practices arising out of the Chinese Exclusion Act era cast identity as an empirical and immutable phenomenon, specifically in response to the racialization of American-born Chinese settlers as duplicitous, through the mechanisms that information is collected, the actual information itself, and the cross-references or connections created between cases. Through tracing this lineage, racialized identification data is identified and theorized as part of hegemonic data regimes. [ABSTRACT FROM AUTHOR]

Recalling the well-known strategy of “following the money” when investigating the underlying power structures and business models of legacy media, this article argues that studies of digital political economies can benefit instead from following the data. Combining perspectives from critical data studies and infrastructure research, we first discuss how direct money flows can be difficult to trace in digital ecosystems, creating a need for alternative analytical approaches for studying and scrutinising contemporary power configurations in digital societies. As a theoretical backdrop, we elaborate on the concept of infrastructural power and apply it in a walkthrough of critical data infrastructures. To illustrate the efficacy of this strategy, we provide perspectives and examples from the political economies of internet infrastructures in Northern Europe and discuss how control over data is translated into economic profit and societal power. In doing so, we argue that increased attention to data infrastructures is needed to advance both critical data and infrastructure studies, improve digital market monitoring, and ground future regulation and policy.

Critical scholarship about datafication reveals the implications of algorithmically driven digital transformations for both social processes and human experiences of subjectivity. Digital transformations embed ontological beliefs in the information systems that drive new organizational processes and are accompanied by techno-positivist discourses that promote the benefits of these schemes. The dual power of new information systems plus strong discursive influences has led to fears that data subjects will come to be defined by data and information systems – that their subjectivity will be subordinated by the algorithm. However, in this paper, we argue that real experiences of data sharing offer a means to reveal actual experiences with subjectification, and that often these experiences are multiple and complex. Drawing on the results of five digital literacy interventions carried out by partner organizations in Chile, Colombia, Paraguay, Peru, and Uruguay in 2021, we consider participants' lived experiences with datafication. Our work reveals how people experience, negotiate, reject, and accept data power's multiple manifestations in ways that strategically mobilize data resources, constituting a fractured data subjectivity that overlaps the bounds of any one information system. This leads us to suggest the idea of the 'unhomed' as a useful concept for understanding data subjectification in the contemporary moment. [ABSTRACT FROM AUTHOR]

In order to diversify the scope of scholarly formats within NECSUS, the new section Data Papers offers a curated platform for publishing commented datasets from film and media studies research projects. It invites researchers to share insights into the often invisible collaborative work of data preparation and dataset collection.

This article presents an approach to practicing ethics when working with large datasets and designing data representations. Inspired by feminist critique of technoscience and recent problematizations of digital literacy, we argue that machine learning models can be navigated in a multi-narrative manner when access to training data is well articulated and understood. We programmed and used web-based interfaces to sort, organize, and explore a community-run digital archive of radio signals. An additional perspective on the question of working with datasets is offered from the experience of teaching image synthesis with freely accessible online tools. We hold that the main challenge to social transformations related to digital technologies comes from lingering forms of colonialism and extractive relationships that easily move in and out of the digital domain. To counter both the unfounded narratives of techno-optimism and the universalizing critique of technology, we discuss an approach to data and networks that enables a situated critique of datafication and correlationism from within.

The article proposes the theoretical category of data arenas as a relational field for strategic actors in diverse areas of the contentious politics of data (Beraldo and Milan, 2019). The paper argues that the conceptualization of data activism needs to be related to the immediate data arena in which the action takes place, in order to select the interactive opportunities and threats for emerging data-driven repertoires of action. To fully work through the relational dynamics of data activism, it is necessary to move from a conceptualization of data infrastructure to the notion of data arenas as an 'open-ended bundle of rules and resources that allows certain kinds of interaction to proceed' (Jasper, 2006: 141). Using the case of environmental data activism, I highlight four key dimensions to study: (a) strategic use of data as capital that differentiates and positions actors, as well as influences their further choices; (b) practices of defining the boundaries of the problem on which the arena focuses and outlining the pool of actors who participate in the process of solving it; (3) sets of relationships among the outlined pool of actors which represent opportunities and threats for the actors, related to the position they occupy within an arena; and (4) power as the ability to control and shape an arena. Data arena approach shed new light on data activism as a relational practice, combining the latest developments in research on data contexts and the political situatedness of data with the emerging field of research on data activism. [ABSTRACT FROM AUTHOR]

The current research examines the emergent literature of Critical Data Studies, and particularly aligns with Michael and Lupton's (2016) manifesto calling for researchers to study the Public Understanding of Big Data. The aim of this paper is to explore Irish stakeholders' narratives on data sharing in agriculture, and the ways in which their attitudes towards different data sharing governance models reflect their understandings of data, the impact that data hold in their lives and in the farming sector, as well as their preferences for how data should be governed within agriculture. Seven focus groups were held in 2019 with Irish stakeholders from a variety of backgrounds, including agri-researchers, those working in SMEs, and farmers of varying ages and sectors. The primary activities carried out during these focus groups centred upon asking participants to discuss four different data sharing governance models, and to work their way through a set of value cards relating to these models. Focus group results are studied using an inductive, data-driven form of thematic analysis (Braun and Clarke 2006). Five primary themes cross-cut these focus groups: 1) Desire for a data intermediary, 2) Reversing the value chain, 3) Categorisation of data, 4) The common good, and 5) Potential danger in data sharing. These themes are explored in the paper through a detailed discussion of the focus group results, in which the authors track the manifestation of these themes across focus groups, and the ways they sometimes morphed or changed depending upon the participating stakeholder group. [ABSTRACT FROM AUTHOR]

This paper examines the knowledge politics and cultures that shape data relations between international aid organisations and Global South public institutions, taking African libraries as an example. International organisations increasingly rely on data from the Global South, purportedly as a resource for development, which has raised valid concerns about the emergence of new practices of data colonialism. One proposed solution is to expand the capacity of Global South institutions to control their own data processes, so they can likewise control the politico-economic relationships that draw on their data. A pan-African library organisation representing 34 countries is exploring this possibility though a multiyear research project to increase library capacity to use data to partner with development aid organisations. However, this work revealed that data colonialism precedes practices of value extraction. In focus groups, a survey of library systems and interviews with aid organisations, aspects of the data cycle are epistemically framed by aid organisations to undercut Global South control, and subtle neocolonial mechanisms encourage libraries to shape their own data cultures according to desires of aid organisations. This underscores the need to expand data neocolonialism as a frame for confronting epistemic injustice by highlighting Western rationalities embedded in data relations. [ABSTRACT FROM AUTHOR]

This article traces the emergence of a 'decolonial turn' in critical technology and data studies that analyzes the transformation of society through data extraction for profit. First, we offer a genealogy of concepts over the last decade from different fields related to this decolonial turn, including work that explores the connection between racism and data. Second, we discuss the commonalities and differences between these approaches and our own proposal, the data colonialism thesis (Couldry & Mejias, 2018, 2019) to clarify how, together, they provide a distinctive take on data and technology. Third, we summarize the most important advantages of the decolonial turn as a transhistorical tool to understand the continuities between colonialism and capitalism. Finally, some wider implications of a decolonial approach to data are explored, and broad theoretical and practical opportunities for resistance are identified. [ABSTRACT FROM AUTHOR]

Data and data visualisations – in the forms of graphs, charts and maps – are becoming an increasingly important feature of social, public and political life. Yet within existing scholarship, the democratic significance of data visualisations has thus far received minimal attention. This article offers a first systematic attempt to make sense of and scrutinise the role of data visualisation in democracy. We apply deliberative systems theory in the analysis of three original case studies to elucidate how data visualisation can integrate into the overall anatomy of democracy, and to normatively assess how data visualisation contributes towards key democratic ideals. Conclusively, we highlight how critical perspectives on power, ideology and epistemology problematise any simplistic account of how data visualisation matters for democracy. [ABSTRACT FROM AUTHOR]

This article discusses digitization weaknesses, biases, and malfunctions to challenge popular, almost hegemonic visions of contemporary technologies. By focusing on examples provided by recent mediated cases, controversies, and critical research about biases, we aim to propose an analysis of anything digital starting from its vulnerabilities, to look beyond polarized deterministic views, both optimistic and pessimistic. The article generates from the thematic track: “Weak Systems. Exploring bias, bugs and the vulnerability of digitization” that took place at the VIII STS Italia Conference. The panel brought together scholars from different backgrounds, including STS, history of technology, sociology of communication and critical data and media studies to discuss instances of technological weaknesses in various contexts. The article sums up some of the panel takeaways and pleas for a cooperative and interdisciplinary effort focusing on “weak systems”.

In this paper, we identify three responses evident in the dialogue regarding the emergence and development of data driven algorithmic governance. The first two responses, ones of access, inclusion and transparency, seek to remedy harms produced by the deployment of advanced digital technologies in public sector service provision. However, with a limited interest in contextualising these technologies relative to the social relations in which they are designed and deployed, these responses ultimately risk misidentifying the sources of harm, thereby reinforcing injustices. The third response, one of data justice, abolition and refusal, seeks to address the limitations to achieving social justice through prioritising digital access, inclusion and transparency. Offering a more transformative response to algorithmic governance, this third response focuses on fundamental questions regarding the deeply unequal power relations, structural inequalities and racism embedded in algorithmic systems, providing a critical repertoire of options for contesting and reconfiguring these relations. While the three responses do not constitute a chronology, we conclude with a discussion of the resurgent interest in refusal as a framework and method to intervene in harmful data-driven algorithmic systems. In so doing, we offer suggestions for how to collectively and institutionally operationalise refusal, alongside abolitionism and data justice, particularly in the area of social welfare provision, and to imagine and bring about alternative social, political and economic systems and relations that are radically transformative.

Public discourses have become sensitive to the ethical challenges of big data and artificial intelligence, as scandals about privacy invasion, algorithmic discrimination, and manipulation in digital platforms repeatedly make news headlines. However, it remains largely unexplored how exactly these complex issues are presented to lay audiences and to what extent news reporting—as a window to tech debates—can instil critical data literacy. The present study addresses this research gap and introduces the concept of “data risks”. The main goal is to critically investigate how societal and individual harms of data-driven technology find their way into the public sphere and are discussed there. The empirical part applies a mixed methods design that combines qualitative and automated content analyses for charting data risks in news reporting sampled from prominent English-speaking media outlets of global reach. The resulting inventory of data risks includes privacy invasion/surveillance, data bias/algorithmic discrimination, cybersecurity, and information disorder. The study posits data risks as communication challenges, highlights shortcomings in public discussions about the issue, and provides stimuli for (practical) interventions that aim at elucidating how datafication and automation can have harmful effects on citizens.

The COVID-19 pandemic prompted a further extension of the sociotechnical logics of digital platforms to every realm of social life. Given the colonialist, oppressive and exploitative dynamics through which digital platforms work, several scholars supported the need to embrace an openly activist role to help individuals contrast the ways in which they are trapped in loops of dependency and trajectorism. Drawing on the results of 40 auto-ethnographic diaries, this paper showcases the usefulness of critical pedagogical techniques in enhancing critical awareness regarding hegemonic datafication structures, while also arguing that despite a good level of consciousness raising, it remains difficult for people to go beyond subalternity and make more concrete changes in personal and collective behaviors. We contend that to break persistent feelings of dependency, it is necessary to go further with a two-step process combining autoethnographic tools, aimed at increasing critical algorithmic awareness, with the development of data science skills that can help individuals acquiring more precise knowledge schemes and scaling down the power of giant corporations, thereby building individual and collective capacities to use data for developing counter-narratives about possible futures. [ABSTRACT FROM AUTHOR]

The world distribution of air pollution is deeply asymmetrical, disproportionately affecting cities in the Global South. One of the main obstacles to accessing environmental justice in Latin America is that citizens lack enough evidence in the form of environmental data. In addition, there is distrust in public data and the institutions that manage them. In this context, this article considers air quality-sensing infrastructures as objects of public deliberation and contestation. We follow a grassroots citizen sensing project in Colombia to analyze how through building alternative community infrastructures, and bringing together citizen science and proactive activism repertoires, people instigated public discussion on environmental governance and influenced change in air quality standards. The case shows the potential of mobilizing citizen-generated data for advancing environmental data justice in contexts characterized by deep structural inequalities, "data corruption," and precarious infrastructure. [ABSTRACT FROM AUTHOR]